Updates on context modeling of occupancy coding for point cloud coding

ABSTRACT

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. A number of contexts associated with the received data is reduced based on occupancy data corresponding to one or more parent nodes and one or more child nodes within the received data. The data corresponding to the point cloud is decoded based on the reduced number of contexts.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.17/231,695 (filed Apr. 15, 2021), which claims priority based on U.S.Provisional Application Nos. 63/034,113 (filed Jun. 3, 2020) and63/066,099 (filed Aug. 14, 2020), the entirety of which are incorporatedby reference herein.

BACKGROUND

This disclosure relates generally to field of data processing, and moreparticularly to point clouds.

Point Cloud has been widely used in recent years. For example, it isused in autonomous driving vehicles for object detection andlocalization; it is also used in geographic information systems (GIS)for mapping, and used in cultural heritage to visualize and archivecultural heritage objects and collections, etc. Point clouds contain aset of high dimensional points, typically of three dimensional (3D),each including 3D position information and additional attributes such ascolor, reflectance, etc. They can be captured using multiple cameras anddepth sensors, or Lidar in various setups, and may be made up ofthousands up to billions of points to realistically represent theoriginal scenes. Compression technologies are needed to reduce theamount of data required to represent a point cloud for fastertransmission or reduction of storage. ISO/IEC MPEG (JTC 1/SC 29/WG 11)has created an ad-hoc group (MPEG-PCC) to standardize the compressiontechniques for static or dynamic point clouds.

SUMMARY

Embodiments relate to a method, system, and computer readable medium fordecoding point cloud data. According to one aspect, a method fordecoding point cloud data is provided. The method may include receivingdata corresponding to a point cloud. A number of contexts associatedwith the received data is reduced based on occupancy data correspondingto one or more parent nodes and one or more child nodes within thereceived data. The data corresponding to the point cloud is decodedbased on the reduced number of contexts.

According to another aspect, a computer system for decoding point clouddata is provided. The computer system may include one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include receiving data correspondingto a point cloud. A number of contexts associated with the received datais reduced based on occupancy data corresponding to one or more parentnodes and one or more child nodes within the received data. The datacorresponding to the point cloud is decoded based on the reduced numberof contexts.

According to yet another aspect, a computer readable medium for decodingpoint cloud data is provided. The computer readable medium may includeone or more computer-readable storage devices and program instructionsstored on at least one of the one or more tangible storage devices, theprogram instructions executable by a processor. The program instructionsare executable by a processor for performing a method that mayaccordingly include receiving data corresponding to a point cloud. Anumber of contexts associated with the received data is reduced based onoccupancy data corresponding to one or more parent nodes and one or morechild nodes within the received data. The data corresponding to thepoint cloud is decoded based on the reduced number of contexts.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2A is a diagram of an octree structure for point cloud data,according to at least one embodiment;

FIG. 2B is a diagram of an octree partition for point cloud data,according to at least one embodiment;

FIG. 2C is a block diagram of parent-level node neighbors for pointcloud data, according to at least one embodiment;

FIG. 2D is a block diagram of parent-node-level contexts for point clouddata, according to at least one embodiment;

FIG. 2E is a block diagram of parent-level neighbors of a current codednode for point cloud data, according to at least one embodiment;

FIG. 2F is a block diagram of parent-node-level contexts for point clouddata, according to at least one embodiment;

FIG. 2G is a block diagram of parent-level neighbors of a current codednode for point cloud data, according to at least one embodiment;

FIG. 2H is a block diagram of child-node-level neighbors for point clouddata, according to at least one embodiment

FIG. 3A is a syntax element for signaling a reduction in point clouddata contexts, according to at least one embodiment;

FIG. 3B is a hash table for coded point cloud geometry information,according to at least one embodiment;

FIG. 4 is an operational flowchart illustrating the steps carried out bya program that decodes point cloud data, according to at least oneembodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1 , according to at leastone embodiment; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6 , according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to point clouds. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, reduce a number of contexts associated with point clouddata. Therefore, some embodiments have the capacity to improve the fieldof computing by allowing for improved point cloud compression anddecompression by considering a relevant subset of neighboring nodes fromamong all neighboring nodes of a current node in point cloud data.

As previously described, point Cloud has been widely used in recentyears. For example, it is used in autonomous driving vehicles for objectdetection and localization; it is also used in geographic informationsystems (GIS) for mapping, and used in cultural heritage to visualizeand archive cultural heritage objects and collections, etc. Point cloudscontain a set of high dimensional points, typically of three dimensional(3D), each including 3D position information and additional attributessuch as color, reflectance, etc. They can be captured using multiplecameras and depth sensors, or Lidar in various setups, and may be madeup of thousands up to billions of points to realistically represent theoriginal scenes. Compression technologies are needed to reduce theamount of data required to represent a point cloud for fastertransmission or reduction of storage. ISO/IEC MPEG (JTC 1/SC 29/WG 11)has created an ad-hoc group (MPEG-PCC) to standardize the compressiontechniques for static or dynamic point clouds.

However, the parent-node-level context modeling of occupancy codingrestricts itself to accessing up to 6 neighboring coded nodes. This maylead to a relatively small number of contexts. Accordingly, only lookingat 6 nearest neighbors will largely restrain the range of receptivefield and thus limit the performance gain from it. Moreover, the contextmodeling of occupancy coding may result in a large number of contexts.However, in terms of hardware implementation, a very large number ofcontexts is not preferable since they consume a large amount of memory.It may be advantageous, therefore, to improve the performance ofparent-node-level context while keeping the number of contexts small byreducing the number of contexts presented.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that compresses and decompresses point cloud databased on reducing an expanded set of contexts associated with the pointcloud data. Referring now to FIG. 1 , a functional block diagram of anetworked computer environment illustrating a point cloud compressionsystem 100 (hereinafter “system”) for compressing and decompressingpoint cloud data. It should be appreciated that FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 5 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 6 and 7 . The server computer 114 may also be locatedin a cloud computing deployment model, such as a private cloud,community cloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for point cloud compressionand decompression is enabled to run a Point Cloud Compression Program116 (hereinafter “program”) that may interact with a database 112. ThePoint Cloud Compression Program method is explained in more detail belowwith respect to FIG. 4 . In one embodiment, the computer 102 may operateas an input device including a user interface while the program 116 mayrun primarily on server computer 114. In an alternative embodiment, theprogram 116 may run primarily on one or more computers 102 while theserver computer 114 may be used for processing and storage of data usedby the program 116. It should be noted that the program 116 may be astandalone program or may be integrated into a larger point cloudcompression and decompression program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 maybe implemented within a single device, or a single device shown in FIG.1 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2A, a diagram of an octree structure 200A isdepicted. In TMC13, if the octree geometry codec is used, the geometryencoding proceeds as follows. First, a cubical axis-aligned bounding boxB is defined by two points (0,0,0) and (2^(M-1), 2^(M-1), 2^(M-1)),where 2^(M-1) defines the size of B and M is specified in the bitstream.The octree structure 200A is then built by recursively subdividing B. Ateach stage, a cube is subdivided into 8 sub-cubes. An 8-bit code, namelythe occupancy code, is then generated by associating a 1-bit value witheach sub-cube in order to indicate whether it contains points (i.e.,full and has value 1) or not (i.e., empty and has value 0). Only fullsub-cubes with a size greater than 1 (i.e., non-voxels) are furthersubdivided.

Referring now to FIG. 2B, a diagram of an octree partition 200B isdepicted. The octree partition 200B may include a two-level octreepartition 202 and a corresponding occupancy code 204, where cubes andnodes in dark indicate they are occupied by points. The occupancy code204 of each node is then compressed by an arithmetic encoder. Theoccupancy code 204 can be denoted as S which is an 8 -bit integer, andeach bit in S indicates the occupancy status of each child node. Twoencoding methods for occupancy code 204 exist in TMC13, i.e., thebit-wise encoding and the byte-wise encoding methods, and the bit-wiseencoding is enabled by default. Either way performs arithmetic codingwith context modeling to encode the occupancy code 204, where thecontext status is initialized at the beginning of the whole codingprocess and is updated during the coding process.

For bit-wise encoding, eight bins in S are encoded in a certain orderwhere each bin is encoded by referring to the occupancy status ofneighboring nodes and child nodes of neighboring nodes, where theneighboring nodes are in the same level of current node. For byte-wiseencoding, S is encoded by referring to an adaptive look up table(A-LUT), which keeps track of the N (e.g., 32) most frequent occupancycodes and a cache which keeps track of the last different observed M(e.g., 16) occupancy codes.

A binary flag indicating whether S is the A-LUT or not is encoded. If Sis in the A-LUT, the index in the A-LUT is encoded by using a binaryarithmetic encoder. If S is not in the A-LUT, then a binary flagindicating whether S is in the cache or not is encoded. If S is in thecache, then the binary representation of its index is encoded by using abinary arithmetic encoder. Otherwise, if S is not in the cache, then thebinary representation of S is encoded by using a binary arithmeticencoder. The decoding process starts by parsing the dimensions of thebounding box B from bitstream. The same octree structure is then builtby subdividing B according to the decoded occupancy codes.

An occupancy code of current node typically has 8 bits, where each bitrepresents whether its i^(th) child node is occupied or not. When codingthe occupancy code of the current node, all the information fromneighboring coded nodes can be used for context modeling. The contextinformation can be further grouped in terms of the partition level anddistance to current node. Without loss of generality, the context indexof the i^(th) child node in current node can be obtained as follows,

-   idx=LUT[i][ctxIdxParent][ctxIdxChild],    where LUT is a look-up table of context indices. ctxIdxParent and    ctxIdxChild denote the LUT indices representing the    parent-node-level and child-node-level neighboring information.

Referring now to FIG. 2C, a block diagram 200C of parent-level nodeneighbors is depicted. According to one or more embodiments, suppose theoctree is traversed in the breadth-first order. Six neighboring nodes ofcurrent node can be utilized as the parent-node-level contextinstallation. The block in center may be the current node and the othersix blocks are may be nearest neighbors where their distance to thecurrent node is 1 block unit. Let P_(i) denote the occupancy status ofthe i^(th) neighbor,

${i.e.},{P_{i} = \{ {\begin{matrix}{1{if}{neighbor}i{is}{occupied}} \\{0{if}{neighbor}i{is}{not}{occupied}}\end{matrix},} }$

where i=0,1, . . . , 5. Then the parent-node-level context index can beobtained as follows,

-   ctxIdxParent=Σ_(i=0) ⁵2^(i)P_(i).    Therefore, if all the neighbors are occupied, the maximum of    ctxIdxParent is 63, indicating the number of contexts in    parent-node-level is 64 in this case.

Referring now to FIG. 2D, a block diagram 200D of parent-node-levelcontexts is depicted. According to one or more embodiments, the octreeis traversed in the breadth-first order. All the nodes in parent-leveloccupancy may be coded and they can be used as contexts in currentlevel. The parent-node-level context accessing method may differ foreach child node with index i. For example, 8 sub-figures may representthe parent-node-level context accessing method for i^(th) child node(i=0,1, . . . , 7) of current coded node. In each sub-figure, thecurrent coded node is highlighted in dark gray and the smaller sub-blockwithin the current node is the i^(th) child node (i=0,1, . . . , 7);each child node has different position relative to the current node. Theparent-node-level context accessing method is different for each childnode depending on its relative position to the current node.

Referring now to FIG. 2E, a block diagram 200E of parent-level neighborsof a current coded node is depicted. In one or more embodiments, whencoding the i^(th) child node (i=0,1, . . . , 7), the 7 adjacent nodes tothe i^(th) child node in parent-level are utilized as contexts. The 7adjacent nodes to the i^(th) child node include 3 nodes that share asame face with the child node and 3 nodes that share a same edge and 1node that shares a same vertex. Since different child nodes refer todifferent sets of parent nodes, when coding current node, 26parent-level neighboring nodes are used in total. Compared to using only6 parent-level neighboring nodes, utilizing 26 parent-level neighboringnodes leverages much more information.

Accordingly, the context index of the i^(th) child node in current nodecan be obtained as:

-   idx=LUT[i][ctxIdxParent][ctxIdxChild],-   ctxIdxParent=Σ_(j=0) ⁶2^(j)P_(i,j)    where P_(i,j) represents the occupancy of the j^(th) adjacent node    in parent-level of the i^(th) child node. P_(i,j) equals to 1 if the    j^(th) adjacent node in parent-level of the i^(th) child node is    occupied and equals to 0 otherwise, where i=0,1, . . . , 7 and    j=0,1, . . . , 6.

To reduce the context number in parent-node-level, in anotherembodiment, the parent-node-level context index may be obtained by,

-   ctxIdxParent=Σ_(i=0) ⁵P_(i),    in which case, the maximum of ctxIdxParent is reduced to 6,    indicating the number of contexts in parent-node-level is reduced to    7 from 64.

To further reduce the context number in parent-node-level, in anotherembodiment, the parent-node-level context is disabled by setting

-   -   ctxIdxParent=0.        In this case, the parent-node-level neighboring information is        not used at all.

Referring now to FIG. 2F, a block diagram 200F of parent-node-levelcontexts is depicted. According to one or more embodiments, the numberof parent-level neighbors can be changed. For example, each child nodemay only utilize 6 nearest parent-level neighbors. 8 sub-figuresrepresent the parent-node-level context accessing method for i^(th)child node (i=0,1, . . . , 7) of the current coded node. In eachsub-figure, the current coded node is highlighted in dark gray and thesmaller sub-block within the current node is the i^(th) child node(i=0,1, . . . , 7); each child node has different position relative tocurrent node. The parent-node-level context accessing method isdifferent for each child node depending on its relative position to thecurrent node. When coding the i^(th) child node (i=0,1, . . . , 7), only6 adjacent nodes to the i^(th) child node in parent-level are utilizedas contexts. The 6 adjacent nodes to the i^(th) child node may include 3nodes that share a same face with the child node and 3 nodes that sharea same edge. Compared to FIG. 2D, the node that shares a same vertexwith child node may not be used in this case.

Referring to FIG. 2G, a block diagram depicting 200G depictingparent-level neighbors of a current coded node, based on theparent-node-level contexts of FIG. 2F, is depicted. In one or moreembodiments, the node that shares a same vertex with child node may notbe used. Accordingly, up to 18 parent-level neighboring nodes may beused in total, reduced from the 26 in FIGS. 2D and 2E. Therefore, thecontext index of the i^(th) child node in current node can be obtainedas:

-   idx=LUT[i][ctxIdxParent][ctxIdxChild],-   ctxIdxParent=Σ_(j=0) ⁵2^(j)P_(i,j)    where P_(i,j) represents the occupancy of the j^(th) adjacent node    in parent-level of the i^(th) child node. P_(i,j) equals to 1 if the    j^(th) adjacent node in parent-level of the i^(th) child node is    occupied and equals to 0 otherwise, where i=0,1, . . . , 7 and    j=0,1, . . . , 5. It may be appreciated that the number of    parent-level neighbors may be further reduced by using only a subset    of neighbors.

To further reduce the number of contexts, one can simplify thederivation of ctxIdxParent. In one embodiment, each child node refers upto 6 parent-level neighbors as shown in FIG. 6 , and the parent-levelcontext index can be obtained as:

-   ctxIdxParent=(Σ_(j=0) ²2^(j)P_(i,j))*4+Σ_(j=3) ⁵P_(i,j)    where P_(i,j) (j=0,1,2) represents the occupancy status of three    parent-level nodes that share a same face with the i^(th) child    node, and P_(i,j) (j=3,4,5) represents the occupancy status of three    parent-level nodes that share a same edge with the i^(th) child    node. In this case, the neighbors sharing a face are of higher    importance and the context index is calculated by taking their all    possible combinations, while the neighbors sharing an edge are of    lower importance and the context index is calculated by just    counting how many of them are occupied. By this means, the maximum    value of ctxIdxParent is reduced from 2⁶−1 to 2⁵−1, therefore the    total number of contexts is reduced by half. Note that other similar    simplification methods can be applied to derive parent-level    context.

Referring now to FIG. 2H, a block diagram 200H of child-node-levelneighbors is depicted. According to one or more embodiments, thechild-node-level neighbors can be defined in many ways. For example, thechild-node-level neighbor nodes that are already coded may be classifiedin terms of their distance to the center node. Without loss ofgenerality, assuming the size of each edge of the child nodes is 1, the

₂ distance to the center is 1, √{square root over (2)} and √{square rootover (3)} from left to right, respectively.

Let C_(i) ¹, C_(i) ^(√{square root over (2)}) and C_(i)^(√{square root over (3)}) denote the occupancy status of the i^(th)neighbor in child-node-level with

₂ distance equal to 1, √{square root over (2)} and √{square root over(3)}, respectively,

${i.e.},{C_{i}^{*} = \{ {\begin{matrix}{1{if}{neighbor}i{is}{occupied}} \\{0{if}{neighbor}i{is}{not}{occupied}}\end{matrix}.} }$

Note that the maximum number of coded child-level neighboring nodes withdistance 1, √{square root over (2)} and √{square root over (3)} may be3, 9 and 7, respectively.

Accordingly, the child-node-level context index can be obtained as:

-   ctxIdxChild=(Σ_(i=0) ²2^(i)C_(i) ¹)*(Σ_(i=0) ⁸2^(i)C_(i)    ^(√{square root over (2)}))*(Σ_(i=0) ⁶2^(i)C_(i)    ^(√{square root over (3)}))    Therefore, the maximum number of contexts in child-node-level is    2¹⁹, which is too much for applications. To reduce the number of    contexts, in one or more embodiments, take partial child-node-level    neighbors may be taken into account. This may reduce the maximum    context number in child-node-level to 2⁷=128.

In another embodiment, the maximum number of contexts may be furthersimplified as:

-   ctxIdxChild=(Σ_(i=0) ²2^(i)C_(i) ¹)*(Σ_(i=0) ⁸C_(i)    ^(√{square root over (2)})+Σ_(i=0) ⁶C_(i)    ^(√{square root over (3)}))    where the maximum context number in child-node-level is reduced to    2³×16=128.

To further reduce the context number, one may only use the three nearestnodes with

₂ distance 1, and the context index is calculated as:

-   ctxIdxChild=(Σ_(i=0) ²2^(i)C_(i) ¹),    where the maximum context number in child-node-level is reduced to    2³=8.

To further reduce the number of contexts, the relationship between theparent-level context ctxIdxParent and the child-node index i can bedecoupled. One can define certain rules to achieve this. In oneembodiment, each child node refers up to 6 parent-level neighbors. Thecontext index of the i^(th) child node in current node can be obtainedas:

-   idx=LUT[ctxIdxParent][ctxIdxChild],-   ctxIdxParent=(Σ_(j=0) ²2^(j)P_(i,j))*4+Σ_(j=3) ⁵P_(i,j)    where P_(i,j) (j=0,1,2) represents the occupancy status of three    parent-level nodes that share a same face with the i^(th) child    node, and P_(i,j) (j=3,4,5) represents the occupancy status of three    parent-level nodes that share a same edge with the i^(th) child    node. Note that the final context index is independent to child    index i. To achieve this, a mapping may be defined for each child    node about the ordering of the neighboring parent-level nodes, i.e.,    P_(i,j). The mapping will be discussed with regard to FIG. 3B. The    decoupling reflects the symmetric geometry between each child node    and its 6 neighboring parent-level nodes. It may be appreciated that    other decoupling methods can be used via defining similar mapping    tables in other embodiments.

According to one or more embodiments, there may be no differentiationbetween different child nodes when calculating the context in order toreduce the number of contexts. Specifically, the context index of thei^(th) child node in current node can be obtained as follows,

-   idx=LUT[ctxIdxParent][ctxIdxChild],    where i is no longer a dimension in the LUT. In other words, all the    child nodes are viewed as the same.

Referring now to FIG. 3A, an syntax element 300A is depicted. In orderto achieve adaptivity to different contents and scenarios, differentcontext reduction strategies may be enabled for different cases.Accordingly, a high-level syntax may be signaled in either sequenceparameter set or geometry parameter set or slice data header orelsewhere to indicate the context reduction level. In terms of the valueof the specified context reduction level, the encoder and decoder canthen decide how to utilize context information from neighboring codednodes. A context reduction level may correspond to a combination schemeof context reduction methods.

In one or more embodiments, the syntax element 300A may be signaled ingeometry parameter set, indicating the context reduction level as shownin the table below. If the context reduction level is 0, meaning thereis no context reduction, the regular context installation is applied. Ifthe context reduction level is 1, a low-level reduction is applied. Ifthe context reduction level is 2, a medium-level reduction is applied.If the context reduction level is 3, a high-level reduction is applied.With the higher number of context reduction level, a smaller number ofcontexts is used. In this case, different context configurations may bechosen in terms of contents and scenarios. Thus,gps_context_reduction_level may specify the context reduction level ingeometry parameter set.

Referring now to FIG. 3B, a hash table 300B is depicted. In one or moreembodiments, the coded geometry occupancy information must be saved incache memory because the later coded nodes need to use this informationas context. The hash table 300B may be used to cache occupancyinformation. For each octree partition depth d, a hash table H_(d) ismaintained, where the key is the Morton code of an octree node in depthd, i.e., M_(i) ^(d)=Morton(x_(i) ^(d),y_(i) ^(d),z_(i) ^(d)), where(x_(i) ^(d),y_(i) ^(d),z_(i) ^(d)) is the 3D coordinates of the octreenode. Using the Morton code M_(i) ^(d) as the key, one can access itsoccupancy value in the hash table H_(d). The occupancy value of theoctree node i in depth d can be obtained as:

${OC}_{i}^{d} = \{ {\begin{matrix}{{{H_{d}( M_{i}^{d} )}{if}{{H_{d}( M_{i}^{d} )}!}} = {N{ULL}}} \\{0{otherwise}}\end{matrix}.} $

When encoding/decoding the occupancy value of current node, theoccupancy information of neighboring nodes is obtained from the hashtable H_(d). After encoding/decoding an occupancy value of the currentnode, the coded occupancy value is then stored in H_(d).

Referring now to FIG. 4 , an operational flowchart illustrating thesteps of a method 400 carried out by a program that compresses anddecompresses point cloud data is depicted.

At 402, the method 400 may include receiving data corresponding to apoint cloud.

At 404, the method 400 may include reducing a number of contextsassociated with the received data based on occupancy data correspondingto one or more parent nodes and one or more child nodes within thereceived data.

At 406, the method 400 may include decoding the data corresponding tothe point cloud based on the reduced number of contexts.

It may be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 5 is a block diagram 500 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 5 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1 ) and server computer 114 (FIG. 1 ) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5 . Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1) and the Point Cloud Compression Program 116 (FIG. 1 ) on servercomputer 114 (FIG. 1 ) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMs 822 (which typically include cache memory). In the embodimentillustrated in FIG. 5 , each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1 ) and the Point Cloud Compression Program 116 (FIG.1 ) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3 G, 4 G, or 5 G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1 ) and thePoint Cloud Compression Program 116 (FIG. 1 ) on the server computer 114(FIG. 1 ) can be downloaded to the computer 102 (FIG. 1 ) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Point CloudCompression Program 116 on the server computer 114 are loaded into therespective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824 ).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 6 , illustrative cloud computing environment 600 isdepicted. As shown, cloud computing environment 600 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 600 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 7 , a set of functional abstraction layers 700provided by cloud computing environment 600 (FIG. 6 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 7 are intended to be illustrative only andembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Point Cloud Compression 96. Point CloudCompression 96 may reduce an expanded set of neighboring nodes for acurrent node for compression and decompression of point cloud data.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of decoding point cloud data, executableby a processor, comprising: receiving data corresponding to a pointcloud; reducing a number of contexts associated with the received databased on (i) occupancy data corresponding to one or more parent nodes ofthe current node and one or more child nodes of the current node withinthe received data, and (ii) a positional relationship of the one or moreparent nodes with respect to the current node; and decoding the datacorresponding to the point cloud based on the reduced number ofcontexts.
 2. The method of claim 1, further comprising caching theoccupancy data based on a hash table.
 3. The method of claim 1, furthercomprising signaling a context reduction level indicated the reducednumber of contexts is included in a sequence parameter set, a geometryparameter set, or a slice data header.
 4. The method of claim 1, whereinthe number of contexts is reduced based on one or more child nodeshaving a shortest distance from the current node.
 5. The method of claim1, wherein the number of contexts is reduced based on considering only asubset of child nodes from among the one or more child nodes.
 6. Themethod of claim 1, wherein the number of contexts is reduced based onnot differentiating between the one or more child nodes.
 7. The methodof claim 1, wherein the number of contexts is reduced based ondiscarding occupancy data associated with the one or more parent nodes.8. The method of claim 1, wherein each parent node from the one or moreparent nodes sharing an edge with the current node has a higher numberof contexts reduced than each parent node from the one or more parentnodes sharing a face with the current node.
 9. A computer system fordecoding point cloud data, the computer system comprising: one or morecomputer-readable non-transitory storage media configured to storecomputer program code; and one or more computer processors configured toaccess said computer program code and operate as instructed by saidcomputer program code, said computer program code including: receivingcode configured to cause the one or more computer processors to receivedata corresponding to a point cloud; reducing code configured to causethe one or more computer processors to reduce a number of contextsassociated with the received data based on (i) occupancy datacorresponding to one or more parent nodes of a current node and one ormore child nodes of the current node within the received data, and (ii)a positional relationship of the one or more parent nodes with respectto the current node; and decoding code configured to cause the one ormore computer processors to decode the data corresponding to the pointcloud based on the reduced number of contexts.
 10. The computer systemof claim 9, further comprising caching code configured to cause the oneor more computer processors to cache the occupancy data based on a hashtable.
 11. The computer system of claim 9, wherein said computer programcode further includes signaling code configured to cause the one or morecomputer processors to signal a context reduction level indicating thereduced number of contexts in a sequence parameter set, a geometryparameter set, or a slice data header.
 12. The computer system of claim9, wherein the number of contexts is reduced based on one or more childnodes having a shortest distance from the current node.
 13. The computersystem of claim 9, wherein the number of contexts is reduced based onconsidering only a subset of child nodes from among the one or morechild nodes.
 14. The computer system of claim 9, wherein the number ofcontexts is reduced based on not differentiating between the one or morechild nodes.
 15. The computer system of claim 9, wherein the number ofcontexts is reduced based on discarding occupancy data associated withthe one or more parent nodes.
 16. The computer system of claim 9,wherein each parent node from the one or more parent nodes sharing anedge with the current node has a higher number of contexts reduced thaneach parent node from the one or more parent nodes sharing a face withthe current node.
 17. A non-transitory computer readable medium havingstored thereon a computer program for decoding point cloud data, thecomputer program configured to cause one or more computer processors to:receive data corresponding to a point cloud; reduce a number of contextsassociated with the received data based on (i) occupancy datacorresponding to one or more parent nodes of a current node and one ormore child nodes of the current node within the received data, and (ii)a positional relationship of the one or more parent nodes with respectto the current node; and decode the data corresponding to the pointcloud based on the reduced number of contexts.
 18. The non-transitorycomputer readable medium of claim 17, wherein the computer program isfurther configured to cause one or more computer processors to cache theoccupancy data based on a hash table.
 19. The non-transitory computerreadable medium of claim 17, further comprising signaling a contextreduction level indicating the reduced number of contexts in a sequenceparameter set, a geometry parameter set, or a slice data header.
 20. Thenon-transitory computer readable medium of claim 17, wherein each parentnode from the one or more parent nodes sharing an edge with the currentnode has a higher number of contexts reduced than each parent node fromthe one or more parent nodes sharing a face with the current node.